61 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at Nature Careers in France
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-Computer Interaction, Software Engineering, Computational Sciences, or a in a similar field Strong foundation in programming, algorithms, and experimental or applied research in a technical domain and
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the critical need for child-centric safe AI by developing a norm-first Belief-Desire-Intention (BDI) architecture where generative models (LLMs) are constrained by machine-readable child-protection policies
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Keywords: theoretical biophysics, machine learning, kinematics, (structural) biology. Context. Machine learning techniques have made significant progress in prediction of favourable structures from
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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(History, Archeology, …). Expected skills: The candidate should have a graduate degree (Master 2 degree). Him/her scholar background should include: • statistical/machine learning, statistical inference
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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and Social Science is seeking a Maître-Assistant (Research Scientist; teaching position) in Teaching and Learning Languages in pre- and primary school to join its team. A special focus should be
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unit (UMR 7248) from UCA and CNRS. Abstract Optical flow estimation is a key task in computer vision, particularly critical for autonomous navigation where accurate motion perception is essential. It can
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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often struggle with domain shift, limited generalization, and the gap between simulation and deployment. These challenges motivate the development of advanced spatio-temporal learning frameworks that can